Employing a corpus of 14,000 Twitter users, this paper explores the link between gender, linguistic style, and social networks. By grouping Twitter users, the dataset dissolves into several styles and topical interests. Combining computational methods and social theory thus provides a new outlook on how gender appears as individuals take a stance with respect to audiences, themes, and conventional gender norms.